Study Suggests That Optimal Use of Bioenergy as Fuel or Electricity Depends on Market and Regulatory Policy Contexts; Higher-Level Assumptions Can Constrain Questions and Affect Results

A new study by a team from UC Berkeley and Stanford University suggests that determining the optimal use of biomass to reduce greenhouse gas emissions—i.e, conversion to fuel molecules or to electrons—depends on market and regulatory contexts that are outside the scope of attributional life cycle assessments (LCA).

A 2009 life cycle analysis by Campbell et al. suggested that converting biomass into electricity for EVs abates more GHG emissions than does converting biomass into liquid fuels for use in today’s conventional vehicles. (Earlier post.) Using that as an example, the new study by Lemoine et al. notes that those results assume that bioelectricity generation displaces gasoline.

Background: Two Styles of LCA
Two styles of LCA have emerged in the literature, the authors note: attributional and consequential.
Attributional LCA is a static analysis based on a product’s supply chain. Consequential LCA considers the net environmental effects induced by a change in production.
Both styles of LCA have been used in recent regulations aiming to reduce GHG emissions from the transportation sector.
Attributional LCA tempts analysts to draw conclusions that ignore the market conditions that affect ultimate environmental outcomes. Consequential LCA, the authors write, recognizes that environmental effects are not limited to a single supply chain’s impacts and generally depend on policy and market contexts; however, the results of consequential LCAs can also mislead if presented without useful framing and sensitivity assessments.

Instead, they argue, under existing institutional and technical arrangements, bioelectricity production does not cause a reduction in gasoline use.

Electricity from all sources flows into the grid and is used to meet instantaneous system-wide demand. Bioelectricity could only displace gasoline if its generation were to increase charging by electrified vehicles already in the fleet or if its generation caused vehicle purchases to shift from gasoline-fueled vehicles to electrified vehicles.

Yet owners of existing electrified vehicles are unlikely to vary their charging habits according to the quantity of bioelectricity on the grid. Further, decisions to purchase electrified vehicles and decisions to use biomass as a primary fuel for electricity are made by different people, at different times, in different places, and without knowledge or concern for the others’ decisions.

Unless vehicle purchasers expect additional bioelectricity generation to substantially reduce electricity prices, it is difficult to imagine plausible technological or policy mechanisms that would link these decisions such that each increase in biomass electricity production is met with an equal expansion of electrified vehicle charging as well as a decline in gasoline vehicle fueling.

A more realistic assessment of GHG mitigation benefits would recognize that an increase in bioelectricity generation in fact recognize that an increase in bioelectricity generation in fact displaces other sources of electricity. The specific benefits depend on the type of electricity replaced, which in turn depends on the structure of the regional electricity market.

—Lemoine et al.

In other words, bioelectricity’s advantage over liquid biofuels depends on the GHG intensity of the electricity displaced. Bioelectricity that displaces coal-fired electricity could reduce GHG emissions, but bioelectricity that displaces wind electricity could increase GHG emissions.

Furthermore, they note, while a proper bioenergy life cycle assessment can inform questions about a bioenergy mandate optimal allocation between liquid fuels and electricity generation, questions about the optimal level of bioenergy use require analyses with differed assumptions about fixed (taken as given) and free (allowed to vary) parameters.

To address this, they developed a typology of modeled assumptions for bioenergy analyses that includes two dimensions of variability: whether the vehicle fleet is fixed or free, and whether the magnitude of overall bioenergy use is fixed or free.

The choice of fixed and free parameters determines whether a model can inform policy decisions about the design, the magnitude, or the appropriateness of a bioenergy mandate. How such a bioenergy mandate should weight bioelectricity versus biofuels depends on the GHG intensity of the electricity that would be displaced. How much biomass should be used for energy depends on the other options for decarbonizing liquid fuels and electricity supply and on the broader social costs of producing energy crops and altering land use patterns.

Finally, whether a climate policy portfolio should include policies specifically aimed at promoting bioenergy and/or electrified vehicles depends on the costs, benefits, and feasibility of such policies relative to other abatement options. The chosen combination of fixed and free parameters not only affects the model’s results but also constrains the set of policy questions to which the results apply.

—Lemoine et al.

Their paper is in press in the ACS journal Environmental Science & Technology.


  • Lemoine, D.M., R.J. Plevin, A.S. Cohn, A.D. Jones, A.R. Brandt, S.E. Vergara, and D.M. Kammen (2010) The climate impacts of bioenergy systems depend on market and regulatory policy contexts. In press. Environmental Science & Technology doi: 10.1021/es100418p

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